Using model-based reasoning for enhanced chatbot communication

Abstract

Chatbots as conversational recommender have gained increasing importance for research and practice with a lot of applications available today. In this paper, we present the methods to support conversational defaults within a human-chatbot conversation that simplifies communication with the purpose of improving the overall recommendation process. In particular, we discuss our model-based reasoning approach for easing user experience during a chat, e.g., in cases where user preferences are mentioned indirectly causing inconsistencies. As a consequence of inconsistencies, it would not be possible for the chatbot to provide answers and recommendations. The presented approach allows for removing inconsistencies during the interactions with the chatbot. Besides the basic foundations, we provide use cases from the intended tourism domain to show the simplification of the conversation process. In particular, we consider recommendations for booking hotels and planning trips.